Traditional　path　planning　algorithms　for　mobile　robots　are　not　effective　to　solve　high-dimensional　problems,　and　suffer　from　slow　convergence　and　complex　modelling.　Therefore,　it　is　highly　essential　to　design　a　more　efficient　algorithm　to　realize　intelligent　path　planning　of　mobile　robots.　This　work　proposes　an　improved　path　planning　algorithm,　which　is　based　on　the　algorithm　of　Soft　Actor-Critic　(SAC).　It　attempts　to　solve　a　problem　of　poor　robot　performance　in　complicated　environments　with　static　and　dynamic　obstacles.　This　work　designs　an　improved　reward　function　to　enable　mobile　robots　to　quickly　avoid　obstacles　and　reach　targets　by　using　state　dynamic　normalization　and　priority　replay　buffer　techniques.　To　evaluate　its　performance,　a　Pygame-based　simulation　environment　is　constructed.　The　proposed　method　is　compared　with　a　Proximal　Policy　Optimization　(PPO)　algorithm　in　the　simulation　environment.　Experimental　results　demonstrate　that　the　cumulative　reward　of　the　proposed　method　is　much　higher　than　that　of　PPO,　and　it　is　also　more　robust　than　PPO.　Copyright　(C)　2022　The　Authors.